提出AC-LIO框架，通过选择性帧内平滑补偿激光雷达运动畸变。
AC-LIO: Towards Asymptotic Compensation for Distortion in LiDAR-Inertial Odometry via Selective Intra-Frame Smoothing
- 基于收敛准则反向传播更新项，渐进补偿残余畸变。
- 在多个数据集上平均RMSE降低30.4%，优于次优方法。
- 适合长期大范围定位与建图，计算开销低。
现有激光雷达-惯性里程计（LIO）方法通常利用惯性测量单元（IMU）积分得到的先验轨迹来补偿激光雷达帧内的运动畸变。然而，先验轨迹与真实轨迹之间的差异会导致残余运动畸变，破坏激光雷达帧与其对应几何环境的一致性。这种不平衡可能使点云配准陷入局部最优，从而加剧长时间、大范围定位中的漂移问题。为此，本文提出一种具有选择性帧内平滑的新LIO框架——AC-LIO。其核心思想是在收敛准则引导下，渐进地反向传播当前更新项，以补偿残余运动畸变，旨在提升离散状态LIO系统的精度，同时保持极低的计算开销。大量实验表明，相比先前方法，本框架在平均均方根误差（RMSE）上进一步降低了约30.4%，显著提升了长时间、大范围定位与建图的准确性。
原文摘要 · Abstract (English)
Existing LiDAR-Inertial Odometry (LIO) methods typically utilize the prior trajectory derived from the IMU integration to compensate for the motion distortion within LiDAR frames. However, discrepancies between the prior and true trajectory can lead to residual motion distortions that compromise the consistency of LiDAR frame with its corresponding geometric environment. This imbalance may result in pointcloud registration becoming trapped in local optima, thereby exacerbating drift during long-term and large-scale localization. To this end, we propose a novel LIO framework with selective intra-frame smoothing dubbed AC-LIO. Our core idea is to asymptotically backpropagate current update term and compensate for residual motion distortion under the guidance of convergence criteria, aiming to improve the accuracy of discrete-state LIO system with minimal computational increase. Extensive experiments demonstrate that our AC-LIO framework further enhances odometry accuracy compared to prior arts, with about 30.4% reduction in average RMSE over the second best result, leading to marked improvements in the accuracy of long-term and large-scale localization and mapping.
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